Previous: What is MetaQTL, Up: General Overview

#### 1.1.2 Definition of the Problem

Since the last decade, the advent of molecular markers have accelerated the pace of discovering the loci which are implied in quantitative trait variation. Quantitative Trait Loci (QTL) mapping usually begins with the collection of genotypic (based on molecular markers) and phenotypic data from a segregating population. First, from the genoptypic data the markers are both ordered and positioned on a genetic map using standard linkage mapping approaches. Secondly, refinement of analytical methods have enabled to detect one ore several QTL on each chromosome (see for instance Lander and Botstein 1989, Zeng 1994). Nevertheless due to the limiting number of individuals and generations in usual experiment this approach generally leads to QTL locations with a confidence interval (CI) around 10 cM (Kearsey 1998) which in plant generally corresponds to a thousand of genes or more.

Due to its relative simplicity and its compelling concept QTL mapping has been widely used and more and more QTL detection results are now available in public databases (e.g in maize at http://www.maizegdb.org). One of the main purpose of these databases was to facilitate the comparison of different QTL detection results by providing both standard description of these results and ontologies (see for instance the trait ontology at http://www.gramene.org/plant\_ontology/). Relevance of comparative analysis of QTL studies have been illustrated by several authors (Khavkin 1997 and 1998, Lin 1995). However these studies often relied on simple descriptive statistics.

QTL congruency study was partially improved thanks to Goffinet and Gerber (2000) who proposed a meta-analysis based approach in order to integrate QTL results from several experiments. Their method makes it possible to evaluate how many “actual” QTL locations underly the distribution of the observed QTL on the genome. This approach has been implemented in BioMercator by Arcade et al. (2004). This software allows user to merge both markers and QTL onto a consensus map by means of an iterative projection procedure. Then the algorithm devised by Goffinet and Gerber (2000) can be applied to evaluate the likelihood of clustering the observed QTL in 1,2,3 or 4 groups. Afterward, the optimal number of clusters is selected by using a Akaike like criterion. Alghough original this approach suffers from the absence of indicator to assess the consensus map quality and from the limiting number of QTL clusters which can be explored.

Based on recent methodological developments, MetaQTL implements a series of Java programs in order to carry out whole-genome QTL meta-analysis. All the programs in MetaQTL are command line programs. Each program does a small job and the user can easily combine the programs as a group to do a complete analysis.